OpenRaiser/Intern-Atlas huggingface dataset:
Destination Prediction Based on Partial Trajectory Data
Venue: arXiv cs.LG 2020
Year: 2020

Two-thirds of the people who buy a new car prefer to use a substitute instead of the built-in navigation system. However, for many applications, knowledge about a user's intended destination and route is crucial. For example, suggestions for available parking spots close to the destination can be made or ride-sharing opportunities along the route are facilitated. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data. The approach follows a three-step procedure: First, a $k$-d tree-based space discretization is performed, mapping GPS locations to discrete regions. Secondly, a recurrent neural network is trained to predict the destination based on partial sequences of trajectories. The neural network produces destination scores, signifying the probability of each region being the destination. Finally, the routes to the most probable destinations are calculated. To evaluate the method, we compare multiple neural architectures and present the experimental results of the destination prediction. The experiments are based on two public datasets of non-personalized, timestamped GPS locations of taxi trips. The best performing models were able to predict the destination of a vehicle with a mean error of 1.3 km and 1.43 km respectively.
Organisational Structure Patterns in Agile Teams: An Industrial Empirical Study
Venue: arXiv cs.CY 2020
Year: 2020

Forming members of an organization into coherent groups or communities is an important issue in any large-scale software engineering endeavour, especially so in agile software development teams which rely heavily on self-organisation and organisational flexibility. To address this problem, many researchers and practitioners have advocated a strategy of mirroring system structure and organisational structure, to simplify communication and coordination of collaborative work. But what are the patterns of organisation found in practice in agile software communities and how effective are those patterns? We address these research questions using mixed-methods research in industry, that is, interview surveys, focus-groups, and delphi studies of agile teams. In our study of 30 agile software organisations we found that, out of 7 organisational structure patterns that recur across our dataset, a single organisational pattern occurs over 37% of the time. This pattern: (a) reflects young communities (1-12 months old); (b) disappears in established ones (13+ months); (c) reflects the highest number of architecture issues reported. Finally, we observe a negative correlation between a proposed organisational measure and architecture issues. These insights may serve to aid architects in designing not only their architectures but also their communities to best support their co-evolution.
Open and Cultural Data Games for Learning
Venue: arXiv cs.CY 2020
Year: 2020

Educators often seek ways to introduce gaming in the classroom in order to break the usual teaching routine, expand the usual course curriculum with additional knowledge, but mostly as a means to motivate students and increase their engagement with the course content. Even though the vast majority of students find gaming to be appealing and a welcome change to the usual teaching practice, many educators and parents doubt their educational value; in this paper, we discuss a card game designed to teach environmental matters to early elementary school students, using open data. We present a comparative study of how the game increased the students' interest for the subject, as well as their performance and engagement to the course, compared with conventional teaching and a Prezi presentation used to teach the same content to other student groups.
ForkBase: Immutable, Tamper-evident Storage Substrate for Branchable Applications
Venue: arXiv cs.DB 2020
Year: 2020

Data collaboration activities typically require systematic or protocol-based coordination to be scalable. Git, an effective enabler for collaborative coding, has been attested for its success in countless projects around the world. Hence, applying the Git philosophy to general data collaboration beyond coding is motivating. We call it Git for data. However, the original Git design handles data at the file granule, which is considered too coarse-grained for many database applications. We argue that Git for data should be co-designed with database systems. To this end, we developed ForkBase to make Git for data practical. ForkBase is a distributed, immutable storage system designed for data version management and data collaborative operation. In this demonstration, we show how ForkBase can greatly facilitate collaborative data management and how its novel data deduplication technique can improve storage efficiency for archiving massive data versions.
Distributed Evolution of Deep Autoencoders
Venue: arXiv cs.NE 2020
Year: 2020

Autoencoders have seen wide success in domains ranging from feature selection to information retrieval. Despite this success, designing an autoencoder for a given task remains a challenging undertaking due to the lack of firm intuition on how the backing neural network architectures of the encoder and decoder impact the overall performance of the autoencoder. In this work we present a distributed system that uses an efficient evolutionary algorithm to design a modular autoencoder. We demonstrate the effectiveness of this system on the tasks of manifold learning and image denoising. The system beats random search by nearly an order of magnitude on both tasks while achieving near linear horizontal scaling as additional worker nodes are added to the system.
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack
Venue: arXiv cs.CL 2020
Year: 2020

Recurrent neural networks (RNNs) are a widely used deep architecture for sequence modeling, generation, and prediction. Despite success in applications such as machine translation and voice recognition, these stateful models have several critical shortcomings. Specifically, RNNs generalize poorly over very long sequences, which limits their applicability to many important temporal processing and time series forecasting problems. For example, RNNs struggle in recognizing complex context free languages (CFLs), never reaching 100% accuracy on training. One way to address these shortcomings is to couple an RNN with an external, differentiable memory structure, such as a stack. However, differentiable memories in prior work have neither been extensively studied on CFLs nor tested on sequences longer than those seen in training. The few efforts that have studied them have shown that continuous differentiable memory structures yield poor generalization for complex CFLs, making the RNN less interpretable. In this paper, we improve the memory-augmented RNN with important architectural and state updating mechanisms that ensure that the model learns to properly balance the use of its latent states with external memory. Our improved RNN models exhibit better generalization performance and are able to classify long strings generated by complex hierarchical context free grammars (CFGs). We evaluate our models on CGGs, including the Dyck languages, as well as on the Penn Treebank language modelling task, and achieve stable, robust performance across these benchmarks. Furthermore, we show that only our memory-augmented networks are capable of retaining memory for a longer duration up to strings of length 160.
Mathematical model of LoRaWAN channel access
Venue: arXiv cs.NI 2020
Year: 2020

While 3GPP has been developing NB-IoT, the market of Low Power Wide Area Networks has been mastered by cheap and simple Sigfox and LoRa/LoRaWAN technologies. Being positioned as having an open standard, LoRaWAN has attracted also much interest from the research community. Specifically, many papers address the efficiency of its PHY layer. However MAC is still underinvestigated. Existing studies of LoRaWAN do not take into account the acknowledgement and retransmission policy, which may lead to incorrect results. In this paper, we carefully take into account the peculiarities of LoRaWAN transmission retries and show that it is the weakest issue of this technology, which significantly increases failure probability for retries. The main contribution of the paper is a mathematical model which accurately estimates how packet error rate depends on the offered load. In contrast to other papers, which evaluate LoRaWAN capacity just as the maximal throughput, our model can be used to find the maximal load, which allows reliable packet delivery.
Autonomous Cooperative Flight Control for Airship Swarms
Venue: arXiv cs.RO 2020
Year: 2020

This work investigates two approaches for the design of autonomous cooperative flight controllers for airship swarms. The first controller is based on formation flight and the second one is based on swarm intelligence strategies. In both cases, the team of airships needs to perform two different tasks: waypoint path following and ground moving target tracking. The UAV platform considered in this work is the NOAMAY airship developed in Brazil. We use a simulated environment to test the proposed approaches. Results show the inherent flexibility of the swarm intelligence approach on both tasks.
In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
Venue: arXiv cs.CV 2020
Year: 2020

Automatically detecting vital signs in videos, such as the estimation of heart and respiration rates, is a challenging research problem in computer vision with important applications in the medical field. One of the key difficulties in tackling this task is the lack of sufficient supervised training data, which severely limits the use of powerful deep neural networks. In this paper we address this limitation through a novel deep learning approach, in which a recurrent deep neural network is trained to detect vital signs in the infrared thermal domain from purely synthetic data. What is most surprising is that our novel method for synthetic training data generation is general, relatively simple and uses almost no prior medical domain knowledge. Moreover, our system, which is trained in a purely automatic manner and needs no human annotation, also learns to predict the respiration or heart intensity signal for each moment in time and to detect the region of interest that is most relevant for the given task, e.g. the nose area in the case of respiration. We test the effectiveness of our proposed system on the recent LCAS dataset and obtain state-of-the-art results.
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
Venue: arXiv cs.LG 2020
Year: 2020

We propose a novel method for analyzing and visualizing the complexity of standard reinforcement learning (RL) benchmarks based on score distributions. A large number of policy networks are generated by randomly guessing their parameters, and then evaluated on the benchmark task; the study of their aggregated results provide insights into the benchmark complexity. Our method guarantees objectivity of evaluation by sidestepping learning altogether: the policy network parameters are generated using Random Weight Guessing (RWG), making our method agnostic to (i) the classic RL setup, (ii) any learning algorithm, and (iii) hyperparameter tuning. We show that this approach isolates the environment complexity, highlights specific types of challenges, and provides a proper foundation for the statistical analysis of the task's difficulty. We test our approach on a variety of classic control benchmarks from the OpenAI Gym, where we show that small untrained networks can provide a robust baseline for a variety of tasks. The networks generated often show good performance even without gradual learning, incidentally highlighting the triviality of a few popular benchmarks.
Quantum circuit synthesis using Householder transformations
Venue: arXiv cs.ET 2020
Year: 2020

The synthesis of a quantum circuit consists in decomposing a unitary matrix into a series of elementary operations. In this paper, we propose a circuit synthesis method based on the QR factorization via Householder transformations. We provide a two-step algorithm: during the first step we exploit the specific structure of a quantum operator to compute its QR factorization, then the factorized matrix is used to produce a quantum circuit. We analyze several costs (circuit size and computational time) and compare them to existing techniques from the literature. For a final quantum circuit twice as large as the one obtained by the best existing method, we accelerate the computation by orders of magnitude.
The Impact of 5G Channel Models on the Performance of Intelligent Reflecting Surfaces and Decode-and-Forward Relaying
Venue: arXiv cs.IT 2020
Year: 2020

An intelligent reflecting surface (IRS) is an array of discrete elements with configurable scattering properties. It has the capability to beamform arriving radio waves to an intended receiver, making it an attractive candidate technology for fifth-generation (5G) communications. A recent study debunked the notion that IRSs can replace relays because a large number of IRS elements is required even to approach the performance of simple single-antenna decode-and-forward (DF) relays. The study introduced 4G channel models into a theoretical framework to obtain simulation results, based on which comparisons between the two schemes were carried out. In this paper, we consider 5G channel models, reflect on the revised results, and argue that IRSs and DF relays can complement each other's strengths and can both have a place in 5G and beyond 5G architectures.
Bus Frequency Optimization: When Waiting Time Matters in User Satisfaction
Venue: arXiv cs.SI 2020
Year: 2020

Reorganizing bus frequency to cater for the actual travel demand can save the cost of the public transport system significantly. Many, if not all, existing studies formulate this as a bus frequency optimization problem which tries to minimize passengers' average waiting time. However, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold is maximized. We prove that this problem is NP-hard, and present an index-based algorithm with $(1-1/e)$ approximation ratio. By exploiting the locality property of routes in a bus network, we propose a partition-based greedy method which achieves a $(1-ρ)(1-1/e)$ approximation ratio. Then we propose a progressive partition-based greedy method to further improve the efficiency while achieving a $(1-ρ)(1-1/e-\varepsilon)$ approximation ratio. Experiments on a real city-wide bus dataset in Singapore verify the efficiency, effectiveness, and scalability of our methods.
Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming
Venue: arXiv cs.AI 2020
Year: 2020

Prior work on generating explanations in a planning and decision-making context has focused on providing the rationale behind an AI agent's decision making. While these methods provide the right explanations from the explainer's perspective, they fail to heed the cognitive requirement of understanding an explanation from the explainee's (the human's) perspective. In this work, we set out to address this issue by first considering the influence of information order in an explanation, or the progressiveness of explanations. Intuitively, progression builds later concepts on previous ones and is known to contribute to better learning. In this work, we aim to investigate similar effects during explanation generation when an explanation is broken into multiple parts that are communicated sequentially. The challenge here lies in modeling the humans' preferences for information order in receiving such explanations to assist understanding. Given this sequential process, a formulation based on goal-based MDP for generating progressive explanations is presented. The reward function of this MDP is learned via inverse reinforcement learning based on explanations that are retrieved via human subject studies. We first evaluated our approach on a scavenger-hunt domain to demonstrate its effectively in capturing the humans' preferences. Upon analyzing the results, it revealed something more fundamental: the preferences arise strongly from both domain dependent and independence features. The correlation with domain independent features pushed us to verify this result further in an escape room domain. Results confirmed our hypothesis that the process of understanding an explanation was a dynamic process. The human preference that reflected this aspect corresponded exactly to the progression for knowledge assimilation hidden deeper in our cognitive process.
Modelling of IP Geolocation by use of Latency Measurements
Venue: arXiv cs.NI 2020
Year: 2020

IP Geolocation is a key enabler for many areas of application like Content Delivery Networks, targeted advertisement and law enforcement. Therefore, an increased accuracy is needed to improve service quality. Although IP Geolocation is an ongoing field of research for over one decade, it is still a challenging task, whereas good results are only achieved by the use of active latency measurements. This paper presents an novel approach to find optimized Landmarks positions which are used for active probing and introduce an improved location estimation. Since a reasonable Landmark selection is important for a highly accurate localization service, the goal is to find Landmarks close to the target with respect to the infrastructure and hop count. Current techniques provide less information about solving this problem as well as are using imprecise models. We demonstrate the usability of our approach in a real-world environment. The combination of an optimized Landmark selection and advanced modulation results in an improved accuracy of IP Geolocation.
Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
Venue: arXiv cs.CV 2020
Year: 2020

We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images. This is achieved via an inter-subject mapping module that transforms original subject landmarks based on an auxiliary subject-related structure. To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images. Our proposed method is extensively evaluated on two public facial image datasets (MAFL, AFLW) with various settings. Experimental results indicate that our method can extract the consistent landmarks for both datasets and achieve better performances compared to the previous state-of-the-art methods quantitatively and qualitatively.
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
Venue: arXiv cs.CV 2020
Year: 2020

In this technical report, we present two novel datasets for image scene understanding. Both datasets have annotations compatible with panoptic segmentation and additionally they have part-level labels for selected semantic classes. This report describes the format of the two datasets, the annotation protocols, the merging strategies, and presents the datasets statistics. The datasets labels together with code for processing and visualization will be published at https://github.com/tue-mps/panoptic_parts.
Spectral Learning on Matrices and Tensors
Venue: arXiv cs.LG 2020
Year: 2020

Spectral methods have been the mainstay in several domains such as machine learning and scientific computing. They involve finding a certain kind of spectral decomposition to obtain basis functions that can capture important structures for the problem at hand. The most common spectral method is the principal component analysis (PCA). It utilizes the top eigenvectors of the data covariance matrix, e.g. to carry out dimensionality reduction. This data pre-processing step is often effective in separating signal from noise. PCA and other spectral techniques applied to matrices have several limitations. By limiting to only pairwise moments, they are effectively making a Gaussian approximation on the underlying data and fail on data with hidden variables which lead to non-Gaussianity. However, in most data sets, there are latent effects that cannot be directly observed, e.g., topics in a document corpus, or underlying causes of a disease. By extending the spectral decomposition methods to higher order moments, we demonstrate the ability to learn a wide range of latent variable models efficiently. Higher-order moments can be represented by tensors, and intuitively, they can encode more information than just pairwise moment matrices. More crucially, tensor decomposition can pick up latent effects that are missed by matrix methods, e.g. uniquely identify non-orthogonal components. Exploiting these aspects turns out to be fruitful for provable unsupervised learning of a wide range of latent variable models. We also outline the computational techniques to design efficient tensor decomposition methods. We introduce Tensorly, which has a simple python interface for expressing tensor operations. It has a flexible back-end system supporting NumPy, PyTorch, TensorFlow and MXNet amongst others, allowing multi-GPU and CPU operations and seamless integration with deep-learning functionalities.
Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality
Venue: arXiv cs.CV 2020
Year: 2020

Augmented reality (AR) or mixed reality (MR) platforms require spatial understanding to detect objects or surfaces, often including their structural (i.e. spatial geometry) and photometric (e.g. color, and texture) attributes, to allow applications to place virtual or synthetic objects seemingly "anchored" on to real world objects; in some cases, even allowing interactions between the physical and virtual objects. These functionalities require AR/MR platforms to capture the 3D spatial information with high resolution and frequency; however, these pose unprecedented risks to user privacy. Aside from objects being detected, spatial information also reveals the location of the user with high specificity, e.g. in which part of the house the user is. In this work, we propose to leverage spatial generalizations coupled with conservative releasing to provide spatial privacy while maintaining data utility. We designed an adversary that builds up on existing place and shape recognition methods over 3D data as attackers to which the proposed spatial privacy approach can be evaluated against. Then, we simulate user movement within spaces which reveals more of their space as they move around utilizing 3D point clouds collected from Microsoft HoloLens. Results show that revealing no more than 11 generalized planes--accumulated from successively revealed spaces with large enough radius, i.e. $r\leq1.0m$--can make an adversary fail in identifying the spatial location of the user for at least half of the time. Furthermore, if the accumulated spaces are of smaller radius, i.e. each successively revealed space is $r\leq 0.5m$, we can release up to 29 generalized planes while enjoying both better data utility and privacy.